The Identification of technology-crossing topics is of great significance. This paper analyzes the content of technology-crossing firstly, then reviews the main methods of technology-crossing topic identification. Finally, it summarizes the advantages and disadvantages of existing research. The current main methods for technology-crossing topic identification include informetrics, citation analysis, network analysis, text mining, knowledge graph, and machine learning, which involve identifications of weak signals, breakthrough technology and disruptive technology. There are three main characteristics in existing research: 1) transition from morphological metrics to text content analysis; 2) association analysis and text mining methods becoming mainstream; 3) comprehensive application of multiple methods and technologies. Future research should strengthen the identification of micro-level thematic features, integrate multiple research methods innovatively, enhance the integration of multivariate data, and develop key technologies for the fusion and application of heterogeneous bigdata in multiple fields.